Overview

Dataset statistics

Number of variables10
Number of observations2159
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory168.8 KiB
Average record size in memory80.1 B

Variable types

Numeric10

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
SBP is highly correlated with DBPHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
HR is highly correlated with DBPHigh correlation
SBP is highly correlated with DBPHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
HR is highly correlated with DBPHigh correlation
Gage is highly correlated with HRHigh correlation
SBP is highly correlated with DBPHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
HR is highly correlated with Gage and 2 other fieldsHigh correlation
RR is highly correlated with HRHigh correlation
Parity has 279 (12.9%) zeros Zeros
Totalrisk has 60 (2.8%) zeros Zeros

Reproduction

Analysis started2022-02-27 16:47:42.700897
Analysis finished2022-02-27 16:48:19.261202
Duration36.56 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Age
Real number (ℝ≥0)

Distinct219
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.6488189
Minimum15
Maximum47.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:19.610169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q125
median30
Q333.7
95-th percentile38.1
Maximum47.5
Range32.5
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation5.426592474
Coefficient of variation (CV)0.1830289595
Kurtosis-0.6275456604
Mean29.6488189
Median Absolute Deviation (MAD)4
Skewness0.03710818114
Sum64011.8
Variance29.44790588
MonotonicityNot monotonic
2022-02-27T17:48:20.025373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25141
 
6.5%
30106
 
4.9%
22104
 
4.8%
31100
 
4.6%
2899
 
4.6%
2997
 
4.5%
2694
 
4.4%
2792
 
4.3%
3389
 
4.1%
2186
 
4.0%
Other values (209)1151
53.3%
ValueCountFrequency (%)
151
 
< 0.1%
161
 
< 0.1%
178
 
0.4%
1815
 
0.7%
18.11
 
< 0.1%
1917
 
0.8%
2012
 
0.6%
2186
4.0%
21.51
 
< 0.1%
21.81
 
< 0.1%
ValueCountFrequency (%)
47.51
 
< 0.1%
451
 
< 0.1%
44.41
 
< 0.1%
44.31
 
< 0.1%
442
0.1%
433
0.1%
42.91
 
< 0.1%
42.62
0.1%
42.51
 
< 0.1%
422
0.1%

Gage
Real number (ℝ≥0)

HIGH CORRELATION

Distinct300
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.5132339
Minimum3
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:20.410415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile12
Q128
median29
Q334
95-th percentile40
Maximum43
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.81044186
Coefficient of variation (CV)0.2646420208
Kurtosis1.234766717
Mean29.5132339
Median Absolute Deviation (MAD)3
Skewness-0.9695730472
Sum63719.072
Variance61.00300205
MonotonicityNot monotonic
2022-02-27T17:48:20.644213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40145
 
6.7%
28127
 
5.9%
34114
 
5.3%
39100
 
4.6%
28.2982
 
3.8%
3282
 
3.8%
28.4379
 
3.7%
3377
 
3.6%
28.1476
 
3.5%
3073
 
3.4%
Other values (290)1204
55.8%
ValueCountFrequency (%)
31
 
< 0.1%
42
 
0.1%
54
 
0.2%
619
0.9%
6.21
 
< 0.1%
6.51
 
< 0.1%
6.61
 
< 0.1%
715
0.7%
7.31
 
< 0.1%
7.51
 
< 0.1%
ValueCountFrequency (%)
431
 
< 0.1%
4215
 
0.7%
41.291
 
< 0.1%
4171
3.3%
40145
6.7%
39.572
 
0.1%
39100
4.6%
38.91
 
< 0.1%
38.862
 
0.1%
38.712
 
0.1%

BMI
Real number (ℝ≥0)

Distinct1087
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.80223529
Minimum15.73
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:20.939219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum15.73
5-th percentile19.3994
Q122.1
median25.21
Q330.029
95-th percentile39.4
Maximum67.1
Range51.37
Interquartile range (IQR)7.929

Descriptive statistics

Standard deviation6.450755113
Coefficient of variation (CV)0.2406797434
Kurtosis2.331661607
Mean26.80223529
Median Absolute Deviation (MAD)3.59
Skewness1.315603529
Sum57866.026
Variance41.61224152
MonotonicityNot monotonic
2022-02-27T17:48:21.200712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.612
 
0.6%
24.2210
 
0.5%
21.310
 
0.5%
28.79
 
0.4%
21.629
 
0.4%
32.49
 
0.4%
30.89
 
0.4%
20.839
 
0.4%
329
 
0.4%
33.28
 
0.4%
Other values (1077)2065
95.6%
ValueCountFrequency (%)
15.731
< 0.1%
15.811
< 0.1%
16.161
< 0.1%
16.331
< 0.1%
16.591
< 0.1%
16.611
< 0.1%
16.691
< 0.1%
16.771
< 0.1%
16.981
< 0.1%
17.041
< 0.1%
ValueCountFrequency (%)
67.11
< 0.1%
59.41
< 0.1%
57.31
< 0.1%
56.071
< 0.1%
55.361
< 0.1%
551
< 0.1%
53.41
< 0.1%
53.21
< 0.1%
52.91
< 0.1%
52.32
0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct461
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.5582856
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:21.447370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile89
Q1105
median114.3
Q3126
95-th percentile153
Maximum199
Range155
Interquartile range (IQR)21

Descriptive statistics

Standard deviation19.53289841
Coefficient of variation (CV)0.1675805225
Kurtosis2.101843075
Mean116.5582856
Median Absolute Deviation (MAD)10.7
Skewness0.9117280779
Sum251649.3387
Variance381.5341205
MonotonicityNot monotonic
2022-02-27T17:48:21.818861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12054
 
2.5%
10952
 
2.4%
11051
 
2.4%
10748
 
2.2%
11147
 
2.2%
11245
 
2.1%
11342
 
1.9%
11442
 
1.9%
10840
 
1.9%
10539
 
1.8%
Other values (451)1699
78.7%
ValueCountFrequency (%)
441
 
< 0.1%
561
 
< 0.1%
682
0.1%
69.561
 
< 0.1%
714
0.2%
721
 
< 0.1%
733
0.1%
744
0.2%
752
0.1%
75.81
 
< 0.1%
ValueCountFrequency (%)
1991
< 0.1%
1981
< 0.1%
1972
0.1%
1961
< 0.1%
1951
< 0.1%
1932
0.1%
1911
< 0.1%
1901
< 0.1%
1892
0.1%
1881
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct409
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.23946596
Minimum10
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:22.132564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile54
Q163
median70
Q378.8
95-th percentile92
Maximum122
Range112
Interquartile range (IQR)15.8

Descriptive statistics

Standard deviation11.93508321
Coefficient of variation (CV)0.1675347092
Kurtosis0.9979576917
Mean71.23946596
Median Absolute Deviation (MAD)8
Skewness0.3940799302
Sum153806.007
Variance142.4462113
MonotonicityNot monotonic
2022-02-27T17:48:22.481508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70124
 
5.7%
6481
 
3.8%
7478
 
3.6%
6869
 
3.2%
6069
 
3.2%
8067
 
3.1%
6267
 
3.1%
7266
 
3.1%
6665
 
3.0%
6559
 
2.7%
Other values (399)1414
65.5%
ValueCountFrequency (%)
101
 
< 0.1%
241
 
< 0.1%
302
 
0.1%
34.951
 
< 0.1%
381
 
< 0.1%
39.71
 
< 0.1%
401
 
< 0.1%
40.531
 
< 0.1%
445
0.2%
44.21
 
< 0.1%
ValueCountFrequency (%)
1221
 
< 0.1%
1201
 
< 0.1%
1161
 
< 0.1%
11013
0.6%
1081
 
< 0.1%
104.31
 
< 0.1%
103.31
 
< 0.1%
1031
 
< 0.1%
1022
 
0.1%
100.831
 
< 0.1%

HR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1146
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.82566744
Minimum53.1
Maximum105.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:22.841168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum53.1
5-th percentile72.0686
Q178.402
median83.008
Q388.42
95-th percentile91.8508
Maximum105.37
Range52.27
Interquartile range (IQR)10.018

Descriptive statistics

Standard deviation6.27127829
Coefficient of variation (CV)0.07571660434
Kurtosis-0.1556874156
Mean82.82566744
Median Absolute Deviation (MAD)4.918
Skewness-0.3076240387
Sum178820.616
Variance39.32893139
MonotonicityNot monotonic
2022-02-27T17:48:23.223723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.20663
 
2.9%
90.87442
 
1.9%
89.67429
 
1.3%
90.49829
 
1.3%
92.3128
 
1.3%
83.00818
 
0.8%
90.30416
 
0.7%
89.61815
 
0.7%
90.35214
 
0.6%
89.57413
 
0.6%
Other values (1136)1892
87.6%
ValueCountFrequency (%)
53.11
< 0.1%
61.571
< 0.1%
63.671
< 0.1%
63.821
< 0.1%
63.981
< 0.1%
64.761
< 0.1%
65.371
< 0.1%
65.9821
< 0.1%
66.171
< 0.1%
66.381
< 0.1%
ValueCountFrequency (%)
105.371
< 0.1%
102.921
< 0.1%
101.481
< 0.1%
99.211
< 0.1%
981
< 0.1%
96.461
< 0.1%
96.231
< 0.1%
95.751
< 0.1%
95.641
< 0.1%
95.4061
< 0.1%

RR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct991
Distinct (%)45.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.75278184
Minimum11.77
Maximum25.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:23.485845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11.77
5-th percentile16.7572
Q117.998
median18.69
Q319.52
95-th percentile20.9612
Maximum25.64
Range13.87
Interquartile range (IQR)1.522

Descriptive statistics

Standard deviation1.36514202
Coefficient of variation (CV)0.0727967739
Kurtosis2.268556758
Mean18.75278184
Median Absolute Deviation (MAD)0.758
Skewness-0.06571126201
Sum40487.256
Variance1.863612734
MonotonicityNot monotonic
2022-02-27T17:48:23.852544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1869
 
3.2%
18.05842
 
1.9%
19.39232
 
1.5%
18.62229
 
1.3%
17.93629
 
1.3%
19.76218
 
0.8%
18.07217
 
0.8%
16.8615
 
0.7%
18.11814
 
0.6%
17.9213
 
0.6%
Other values (981)1881
87.1%
ValueCountFrequency (%)
11.771
< 0.1%
12.091
< 0.1%
12.271
< 0.1%
12.411
< 0.1%
12.641
< 0.1%
13.21
< 0.1%
13.371
< 0.1%
13.41
< 0.1%
13.591
< 0.1%
14.211
< 0.1%
ValueCountFrequency (%)
25.641
< 0.1%
24.451
< 0.1%
23.971
< 0.1%
23.91
< 0.1%
23.351
< 0.1%
23.221
< 0.1%
23.191
< 0.1%
23.181
< 0.1%
22.951
< 0.1%
22.8182
0.1%

Parity
Real number (ℝ≥0)

ZEROS

Distinct27
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.774339972
Minimum0
Maximum12
Zeros279
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:24.177659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.529720763
Coefficient of variation (CV)0.8621350966
Kurtosis5.799033421
Mean1.774339972
Median Absolute Deviation (MAD)1
Skewness1.96338727
Sum3830.8
Variance2.340045614
MonotonicityNot monotonic
2022-02-27T17:48:24.524726image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1811
37.6%
2497
23.0%
0279
 
12.9%
3210
 
9.7%
4103
 
4.8%
551
 
2.4%
625
 
1.2%
1.423
 
1.1%
1.221
 
1.0%
719
 
0.9%
Other values (17)120
 
5.6%
ValueCountFrequency (%)
0279
 
12.9%
0.24
 
0.2%
0.45
 
0.2%
0.63
 
0.1%
0.812
 
0.6%
1811
37.6%
1.221
 
1.0%
1.423
 
1.1%
1.617
 
0.8%
1.818
 
0.8%
ValueCountFrequency (%)
121
 
< 0.1%
111
 
< 0.1%
102
 
0.1%
99
 
0.4%
812
 
0.6%
719
 
0.9%
625
 
1.2%
551
2.4%
4103
4.8%
3.62
 
0.1%

GDM
Real number (ℝ≥0)

Distinct265
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.76462251
Minimum48.6
Maximum147.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:24.918944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum48.6
5-th percentile70.2
Q174.16
median77.4
Q380.36
95-th percentile87.12
Maximum147.6
Range99
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation6.147772605
Coefficient of variation (CV)0.07905616213
Kurtosis18.02880582
Mean77.76462251
Median Absolute Deviation (MAD)3.24
Skewness2.309258145
Sum167893.82
Variance37.795108
MonotonicityNot monotonic
2022-02-27T17:48:25.244298image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73.8114
 
5.3%
75.6109
 
5.0%
79.2104
 
4.8%
77.496
 
4.4%
7272
 
3.3%
8169
 
3.2%
76.3253
 
2.5%
78.1248
 
2.2%
82.846
 
2.1%
77.0444
 
2.0%
Other values (255)1404
65.0%
ValueCountFrequency (%)
48.61
 
< 0.1%
59.41
 
< 0.1%
601
 
< 0.1%
61.22
 
0.1%
622
 
0.1%
636
0.3%
641
 
< 0.1%
64.810
0.5%
651
 
< 0.1%
663
 
0.1%
ValueCountFrequency (%)
147.61
< 0.1%
140.41
< 0.1%
129.61
< 0.1%
115.21
< 0.1%
1141
< 0.1%
111.61
< 0.1%
109.81
< 0.1%
1091
< 0.1%
106.21
< 0.1%
104.42
0.1%

Totalrisk
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.111162575
Minimum0
Maximum5
Zeros60
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2022-02-27T17:48:25.533517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.022300233
Coefficient of variation (CV)0.4842356744
Kurtosis-0.3832023649
Mean2.111162575
Median Absolute Deviation (MAD)1
Skewness0.3259905181
Sum4558
Variance1.045097767
MonotonicityNot monotonic
2022-02-27T17:48:25.801370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2802
37.1%
1587
27.2%
3490
22.7%
4203
 
9.4%
060
 
2.8%
517
 
0.8%
ValueCountFrequency (%)
060
 
2.8%
1587
27.2%
2802
37.1%
3490
22.7%
4203
 
9.4%
517
 
0.8%
ValueCountFrequency (%)
517
 
0.8%
4203
 
9.4%
3490
22.7%
2802
37.1%
1587
27.2%
060
 
2.8%

Interactions

2022-02-27T17:48:15.547339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:50.137184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:52.518199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:55.028656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:57.342685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:00.764793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:03.868497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:06.850561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:09.587909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:12.566185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:15.845379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:50.372547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:52.853355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:55.256299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:57.682774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:01.063721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:04.125354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:07.082602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:09.893827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:12.832849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:16.145483image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:50.586763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:53.219135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:55.547821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:58.024798image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:01.305750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:04.567739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:07.374965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:10.116132image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:13.103774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:16.448142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:50.848443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:53.494675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:55.758904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:58.379057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:01.548543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:04.994076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:07.643703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:10.395259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:13.409049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:16.747116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:51.064405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:53.704541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:56.102942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:58.775506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:02.007120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:05.240681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:07.851241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:10.670146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:13.773208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:16.985108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:51.260126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:53.944625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:56.296979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:58.980719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:02.393513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:05.487630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:08.140463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:11.035650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:13.954600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:17.227402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:51.480277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:54.149595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:56.486128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:59.422301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:02.751827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:05.830911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:08.378097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:11.398265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:14.226694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:17.508980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:51.693958image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:54.350560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:56.671400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:59.752325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:03.011688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:06.132490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:08.628125image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:11.719801image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:14.665178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:17.770239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:52.037980image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:54.544623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:56.861805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:00.118120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:03.329810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:06.407675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:08.882564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:12.012046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:14.939871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:18.028803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:52.279835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:54.841623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:47:57.070063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:00.425199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:03.622299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:06.617899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:09.160900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:12.260400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-02-27T17:48:15.156373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-02-27T17:48:26.044520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-27T17:48:26.489718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-27T17:48:26.955213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-27T17:48:27.316131image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-27T17:48:18.553900image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-27T17:48:19.017439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

AgeGageBMISBPDBPHRRRParityGDMTotalrisk
038.012.4321.88109.0573.0681.8820.282.279.802
137.011.5725.51112.2572.0377.8923.973.677.803
225.012.8622.5888.4851.1777.5220.482.077.323
334.011.8621.3098.6258.1989.4415.872.077.001
432.011.5724.91113.2061.4673.1022.481.677.201
525.012.4323.53101.4861.3961.5718.221.683.402
633.012.1418.9494.9860.5686.7820.151.674.881
728.013.1424.58116.9874.5674.5719.651.676.201
830.012.5722.6698.7960.1876.1819.791.879.801
933.012.0023.15105.4663.8565.3718.833.476.321

Last rows

AgeGageBMISBPDBPHRRRParityGDMTotalrisk
214925.034.026.0108.062.083.05417.3943.078.123
215026.029.043.3181.088.090.49817.9360.080.363
215137.033.036.5128.088.090.75416.4061.078.483
215239.028.032.0137.090.090.08617.3027.080.283
215326.032.037.5106.076.080.71818.2921.080.282
215422.025.028.488.058.083.56019.1162.076.322
215533.028.022.589.062.088.28416.2281.075.963
215627.031.036.8122.070.080.29420.3782.090.362
215730.034.026.2121.072.086.61620.1825.075.602
215823.025.030.493.070.086.58219.2461.077.402

Duplicate rows

Most frequently occurring

AgeGageBMISBPDBPHRRRParityGDMTotalrisk# duplicates
022.031.031.96110.070.081.4516.8621.075.622